plot.cv.grpnet | R Documentation |
Plots the mean cross-validation error, along with lower and upper standard deviation curves, as a function of log(lambda)
.
## S3 method for class 'cv.grpnet'
plot(x, sign.lambda = 1, nzero = TRUE, ...)
x |
Object of class "cv.grpnet" |
sign.lambda |
Default plots |
nzero |
Should the number of non-zero groups be printed on the top of the x-axis? |
... |
Additional arguments passed to the |
Produces cross-validation plot only (i.e., nothing is returned).
No return value (produces a plot)
Syntax and functionality were modeled after the plot.cv.glmnet
function in the glmnet package (Friedman, Hastie, & Tibshirani, 2010).
Nathaniel E. Helwig <helwig@umn.edu>
Friedman, J., Hastie, T., & Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 33(1), 1-22. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v033.i01")}
Helwig, N. E. (2024). Versatile descent algorithms for group regularization and variable selection in generalized linear models. Journal of Computational and Graphical Statistics. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/10618600.2024.2362232")}
cv.grpnet
for k-fold cross-validation of lambda
plot.grpnet
for plotting the regularization path
# see 'cv.grpnet' for plotting examples
?cv.grpnet
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